fromfrom sklearn.svm import SVCvclf=Votingclassifier(estimators=[( 'nfc',rfc),( 'clf',clf),( 'svm',svc(kernel= 'linear ',prol.vclf.fit(X,Y)predictions=vclf.predict(X test)def score(predictions,labels):return np.sum([1 if p==a else @for p, a in zip(predictions,labels)])/len(labels)methods=["random forest", "dicision tree", "support vector machine"]reses=[rfc test,dtc test,svm test]for mathod, res in zip(methods,reses):print("Accuracy:%.4f[%s]"%(score(np.squeeze(res),labels),method))
时间: 2023-11-28 12:49:07 浏览: 37
这段代码存在一些语法错误和缺少定义的变量,我来给你修改一下:
```
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
# 定义模型和数据
rfc = RandomForestClassifier()
dtc = DecisionTreeClassifier()
svc = SVC(kernel='linear')
X_train = ...
y_train = ...
X_test = ...
y_test = ...
# 构建 VotingClassifier
vclf = VotingClassifier(estimators=[('nfc', rfc), ('clf', dtc), ('svm', svc)])
vclf.fit(X_train, y_train)
# 预测和评估
def score(predictions, labels):
return np.sum([1 if p == a else 0 for p, a in zip(predictions, labels)]) / len(labels)
models = ["Random Forest", "Decision Tree", "Support Vector Machine"]
results = [rfc.predict(X_test), dtc.predict(X_test), svc.predict(X_test)]
for model, result in zip(models, results):
print("Accuracy: %.4f [%s]" % (score(result, y_test), model))
```
这段代码的作用是构建一个投票分类器,将三种不同的分类器(随机森林、决策树、支持向量机)进行投票,得出最终的分类结果。然后对测试集进行预测并评估每个模型的准确率。